Search

The Qinghai-Tibet Plateau (QTP) encompasses areas with a remarkably high degree of biodiversity, harboring exceptional species-rich radiations. How these radiations formed by interacting with geology, climate and ecology remains seldom examined.We investigate the roles of abiotic (environmental) and biotic (species-intrinsic) factors in driving radiating diversification of Saussurea (Asteraceae) by deploying a number of time-dependent, paleoenvironment-dependent and trait-dependent models, as well as ecological distribution data.We show that three main clades of Saussurea begin to diversify in the Miocene almost simultaneously, with increasing diversification rates toward the present and negative dependence to paleotemperature. Acceleration in diversification rates are correlated with adaptive traits, as well climate lability, niche breadth and species range.We conclude that fluctuation of paleoclimate along with complex QTP environments provided opportunities for increased diversification rates of Saussurea with diverse adaptive traits, highlighting the importance of combinations of clade-specific traits and ecological niches in driving rapid radiation.

Dew plays a vital role in ecosystem processes in arid and semi-arid regions and is expected to be affected by climate warming. Infrared heater warming systems have been widely used to simulate climate warming effects on ecosystem. However, how this warming system affects dew formation has been long ignored and rarely addressed. In a typical alpine grassland ecosystem on the Northeast of the Tibetan Plateau, we measured dew amount and duration by artificial condensing surfaces, leaf wetness sensors and in situ dew formation on plants from 2012 to 2017. We also measured plant traits related to dew conditions. The results showed that (1) warming reduced the dew amount by 41.6%-91.1% depending on the measurement method, and reduced dew duration by 32.1 days compared to the ambient condition. (2) Different plant functional groups differed in dew formation. (3) Under the infrared warming treatment, the dew amount decreased with plant height, while under the ambient conditions, the dew amount showed the opposite trend. We concluded that warming with an infrared heater system greatly reduces dew formation, and if ignored, it may lead to overestimation of the effects of climate warming on ecosystem processes in climate change simulation studies.

Dew, as an important contribution of non-rainfall water (NRW), plays a vital role in ecosystem processes in arid and semi-arid regions and is expected to be affected by climate warming. Infrared heater warming systems have been widely used to simulate climate warming effects on ecosystems. However, how this warming system affects dew formation has been long ignored and rarely addressed. In a typical alpine grassland ecosystem on the northeast of the Tibetan Plateau, we measured dew amount and duration using three independent methods: artificial condensing surfaces, leaf wetness sensors and in situ dew formation on plants from 2012 to 2017. We also measured plant traits related to dew conditions. The results showed that (1) warming reduced the dew amount by 41.6%-91.1% depending on the measurement method, and reduced dew duration by 32.1 days compared to the ambient condition. (2) Different plant functional groups differed in dew formation. (3) Under the infrared warming treatment, the dew amount decreased with plant height, while under the ambient conditions, the dew amount showed the opposite trend. We concluded that warming with an infrared heater system greatly reduces dew formation, and if ignored, it may lead to overestimation of the effects of climate warming on ecosystem processes in climate change simulation studies.

For thousands of years, humans have created different types of terraces in different sloping conditions, meant to mitigate flood risks, reduce soil erosion and conserve water. These anthropogenic landscapes can be found in tropical and subtropical rainforests, deserts, and arid and semiarid mountains across the globe. Despite the long history, the roles of and the mechanisms by which terracing improves ecosystem services (ESs) remain poorly understood. Using literature synthesis and quantitative analysis, the worldwide types, distributions, major benefits and issues of terracing are presented in this review. A key terracing indicator, defined as the ratio of different ESs under terraced and non-terraced slopes (δ), was used to quantify the role of terracing in providing ESs. Our results indicated that ESs provided by terracing was generally positive because the mean values of δ were mostly greater than one. The most prominent role of terracing was found in erosion control (11.46 ± 2.34), followed by runoff reduction (2.60 ± 1.79), biomass accumulation (1.94 ± 0.59), soil water recharge (1.20 ± 0.23), and nutrient enhancement (1.20 ± 0.48). Terracing, to a lesser extent, could also enhance the survival rates of plant seedlings, promote ecosystem restoration, and increase crop yields. While slopes experiencing severe human disturbance (e.g., overgrazing and deforestation) can generally become more stable after terracing, negative effects of terracing may occur in poorly-designed or poorly-managed terraces. Among the reasons are the lack of environmental legislation, changes in traditional concepts and lifestyles of local people, as well as price decreases for agricultural products. All of these can accelerate terrace abandonment and degradation. In light of these findings, possible solutions regarding socio-economic changes and techniques to improve already degraded terraces are discussed.

Land preparation (e.g., leveled ditches, leveled benches, adversely graded tableland, and fish-scale pits) is one of the most effective ecological engineering practices to reduce water erosion in the Loess Plateau, China. Land preparation greatly affects soil physicochemical properties. This study investigated the influence of different land preparation techniques during vegetation restoration on soil conditions, which remained poorly understood to date. Soil samples were collected from depths of 0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm, in the typical hilly watershed of Dingxi City, Loess Plateau. Soil bulk density (BD), soil organic matter (SOM), and total nitrogen (TN) were determined for different land preparations and vegetation type combinations. Fractal theory was used to analyze soil particle size distribution (PSD). (1) The effect of land preparation on soil properties and PSD varied with soil depth. For each land preparation category, SOM and TN values showed a significant difference between the top soil layer and the underlying soil depths. (2) The fractal dimension of PSD showed a significant positive correlation with clay and silt content, but a significant negative correlation with sand content. (3) The 20 cm soil layer was a boundary that distinguished the explanatory factors, with land preparation and vegetation type as the controlling factors in the 0–20- and 20–100-cm soil layers, respectively. Land preparation and vegetation type significantly influenced soil properties, with 20 cm soil depth being the boundary for these two factors. This study provided a foundation for developing techniques for vegetation restoration in water-limited ecosystems.

Soil moisture plays a key role in vegetation restoration and ecosystem stability in arid and semiarid regions. The response of soil moisture to rainfall pulses is an important hydrological process, which is strongly influenced by land use during the implementation of vegetation restoration. In this study, vertical soil moisture variations of woodland (Pinus tabulaeformis), native grassland (Stipa bungeana), shrubland (Hippophea rhamnoides), cropland (Triticum aestivum) and artificial grassland (Onobrychis viciaefolia) in five soil profiles were monitored in a typical loess hilly area during the 2010 growing season. The results demonstrated that rainfall pulses directly affected soil moisture variation. A multi-peak pattern of soil moisture appeared during the growing season, notably in the surface soil layer. Meanwhile, the response of each vegetation type to rainfall was inconsistent, and a time-lag effect before reaching the peak value was detected, following each heavy rainfall event. The response duration of soil moisture, however, varied markedly with the size of rainfall events. Furthermore, higher soil water content was detected in grassland and shrubland. Woodland was characterized by relatively lower soil moisture values throughout the investigation period. Our research suggests that vegetation restoration efforts should give priority to grassland and shrubland at the research site. We suggest that more studies should be focused on the characteristics of community structure and spatial vegetation distribution on soil moisture dynamics, particularly within the grass and shrub ecosystems.

A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA) extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM) and the classifier parameters are optimized with quantum simulated annealing algorithm (QSA). In order to verify the effectiveness of the proposed algorithm, the normal SVM method has been chosen as comparing algorithm. The experimental results show that the proposed method can effectively extract the eigenfeatures and improve the classification accuracy of pancreatic images.

A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized N-dimensional Principal Component Analysis (GND-PCA) is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM) algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases.